Iceberg Detection in Dual-Polarized C-Band SAR Imagery by Segmentation and Nonparametric CFAR (SnP-CFAR)

We propose an unsupervised method for iceberg detection over sea ice-free waters. The algorithm is based on the segmentation and nonparametric constant false alarm rate (SnP-CFAR) approach. Unlike in parametric CFAR detection, in our method, there is no need to define target, guard, and background areas explicitly. Instead, we apply the CFAR detection to the pixels within each detected segment and the background is formed of the nearby pixels not included in the target segment. By using nonparametric background probability density function (PDF) estimates, we also eliminate the need of assuming a specific type of a background PDF. We compared the detection results with the operational Danish Meteorological Institute (DMI) Gamma-CFAR algorithm results. The results were evaluated against icebergs manually identified by the Finnish Meteorological Institute (FMI) Ice analysts. Our method also exhibits a reduced number of false alarms. We present results of iceberg detection based on the SAR channel-cross-correlation (CCC). CCC was able to distinguish many of the true targets with a low number of false alarms. However, CCC seems to miss some of the true targets and its main use would be in confirming iceberg observations.

Automated summary

A new unsupervised method for iceberg detection over sea ice-free waters is proposed in this study, which shows a reduced number of false alarms compared to existing algorithms while effectively detecting icebergs. The algorithm is based on the segmentation and nonparametric constant false alarm rate (SnP-CFAR) approach.